Multi-objective optimization of a long-haul truck hybrid operational strategy and a predictive powertrain control system

An optimum operating strategy is mandatory for early amortization of the expensive Hybrid Electric Vehicle (HEV) powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a huge impact on the Total Cost of Ownership (TCO), which is the key entrepreneurial figure in the transportation business. Combined with route information, a Predictive Cruise Control (PPC) System increases the fuel-saving effects. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a Genetic Algorithm (GA). The contradiction between minimizing the fuel consumption and simultaneously maximizing the vehicle speed in order to decrease time-related fixed costs has to be solved. This leads to a Multi-Objective Problem (MOP). The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of recuperating, boosting, shifting the load point (SLP) and electric drive only. The following methodology helps to answer the search for an optimum control parameter setup combining the operational strategy and the PPC System in long-haul operations. This paper describes the model building, simulation and optimization of a rule-based control strategy. The route profile and fuel consumption of an Internal Combustion Engine (ICE) truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving effects of up to 11% with simultaneously increasing the vehicle speed were accomplished.